Overall, efficiency represents a more practical side of an estimator: the error we can expect it to have w.r.t. the true value \(p\)  after \(n\)  samples.
The effect the number of samples has on this measure can be seen as the speed of convergence.
While most existing work on learning from user interactions focuses on correcting for bias, they often also apply propensity clipping or self-normalization which introduce a little bias to greatly reduce variance [1]}, [2]}, [3]}, [4]}, [5]}.
